ETL

ETL is a term used in data engineering and management that stands for “extract, transform, and load.” The ETL process is a key part in integrating data from various sources as it allows organizations to consolidate and standardize data. After the ETL process has been performed, data is then in a centralized location and in a format that’s ready for data analysis.

Below is an overview of each step in the ETL process:

• Extract: The first step is to extract data from its original source. During this step, both structured and unstructured data is collected into a single data warehouse. Typically, the extraction process will be handled automatically with the use of data management tools as opposed to manually collected.
• Transform: The next step is to transform the raw data from various sources into a standardized format. This step is done to ensure data consistency, quality, and accessibility. In particular, transforming data involves several sub-processes such as cleaning, standardizing, sorting, and verifying its accuracy.
• Load: Finally, the last step in ETL is to load the standardized data into a single, centralized location such as a data warehouse. After this step, the data is now ready for analysis.

A similar data processing approach is called ELT— extract, load, transform—in which the data processing step is performed after it has been loaded into a database.

Since very few businesses rely on a single source of data, ETL and other data management processes allow organizations to unify and consolidate their data for the purpose of analysis and extracting actionable insights. The ETL process is a core component of businesses using data to improve their decision marketing process.

As SAS highlights, a few of the common use cases of ETL include:

• Providing historical context for businesses when ETL is used with a data warehouse
• Consolidating and standardizing data to make it easier for analysts to extract insights
• Improving productivity as the ETL processes can allow data professionals to move data within an organization without having to write code
• Integrating data from various sources which facilitates data streaming
• Ensuring the accuracy and validity of data: ETL can be used to audit data before it is stored in a data warehouse

As Advertity writes, the goal of ETL in the context of marketing is to increase efficiency: 

At the end of the day, ETL is there to make the job of marketers easier and more efficient.

In short, ETL allows businesses to make use of all their data for the purpose of making data-driven decisions, regardless of the original source or format.

Singular provides app businesses with marketing ETL in order to unify and organize data with best-in-class schemas that are loaded where you want, when you want them. Singular’s marketing ETL tool automates data pipeline management, enabling both faster decision making and 100% data coverage. In particular, with automated data standardization and combining this turns siloed data into actionable insights without having to write code. Similarly, the platform allows you to connect to any data source that the marketing team uses across desktop, mobile, and offline.

Below is a brief overview of how our marketing ETL works:

• Collect: First, the ETL extracts cost and marketing data from any source with a variety of flexible collection methods.

• Transform: Next, the data is cleaned, organized, and enriched for a holistic and accurate view of campaign performance.
• Load: Finally, the cleaned data is loaded directly into a data warehouse in order to facilitate internal reporting of your marketing efforts.

As stated in our help center on the Singular Data Destinations (ETL):

Singular can feed data directly into your data warehouse, storage service, or BI platform, where you can use your own BI and visualization tools to process and analyze the data.

You can learn more about our data integrations and partners here.